Vice President, Build — Data, Engineering & AI

We’re in relentless pursuit of breakthroughs that change patients’ lives. We innovate every day to make the world a healthier place. To fully realize Pfizer’s purpose – Breakthroughs that change patients’ lives – we have established a clear set of expectations regarding “what” we need to achieve for patients and “how” we will go about achieving those goals. Pfizer offers competitive compensation and benefits programs designed to meet the diverse needs of our colleagues.

The VP, Build — Data, Engineering & AI is the owner of the International Commercial build engine, accountable for turning prioritized demand into trusted, reusable and production-grade data, AI agents, solutions and technical services. The role owns the International commercial data foundation, engineering pool, solution architecture, AI evaluation, AgentOps, production run, technical standards, vendor/Systems Integrator delivery model and CIO-platform integration. Its mandate is to deliver speed without fragmentation, reuse without bureaucracy, and innovation with evidence, reliability, compliance and cost discipline.

This is a Vice President-level enterprise build role spanning the full path from data foundation to production AI. The role operates with senior stakeholders across International Commercial, CIO, CMO, Legal/Compliance, Finance, markets, strategic vendors and SI partners, with accountability for technical strategy, delivery capacity, production quality, run economics and integration with Pfizer’s enterprise platform standards.

Key responsibilities & accountabilities:

• Build the trusted commercial data foundation: L1–L4 squads working to one standard (the four data layers, raw to ready‑to‑use), including reporting & BI and the knowledge & unstructured squad that underpins retrieval quality for the AI Capability Library (AICL).
• Own semantic standards, inheritance rules and AI-ready criteria through the Data Architect; run the Collibra dictionary, master & reference data operations and the governance council.
• Own data quality and observability: shift‑left checks, lineage and monitoring built in, not bolted on.
• Run data BAU — incidents, refreshes and access — baselined before any cost reduction is taken.
• Own data products and their owners (global and above‑market, including the Customer / CRM and CFC (Customer Facing Colleagues) reporting products), with business data stewards dotted in from markets and functions.
• Manage the data‑supply vendor relationships (IQVIA, Komodo, Optum, Veeva, etc): data and delivery contracts, one door to Procurement.
• Own solution architecture: how solutions compose the data, the AICL and the platform, including model‑tier selection — mirroring the Data Architect on the data side.
• Run one shared, deployable engineering pool crewed into pods; Systems Integration partners and contractors crew in—never as standing outside teams.
• Own product and delivery management: roadmap and translation on the way in; scrum and programme delivery on the way through.
• Build the agent library and standards; deliver the reusable data agents of the AICL and their agent‑facing data contracts.
• Run AI evaluation and assurance independently of the builders: evals, red‑team, model‑tier proof and the clearance evidence pack.
• Own AgentOps and technical run: agents and classic ML in production, AI cost operations, and solution run & support.

Success measures include:

• Prioritized use cases delivered to production with named owner, evidence pack, run model and sunset criteria.
• Data quality, lineage and observability coverage across priority data products.
• Time from funded demand to production.
• Production reliability, incident rate and support performance.
• AI evaluation coverage, red‑team completion and model-tier proof before ship.
• Cost-to-serve, model-routing savings and reuse of AICL / agent patterns across markets.

Qualifications:

• 15+ years across data engineering and AI/ML or software delivery, with senior leadership of both data and engineering organisations.
• A track record of building governed data platforms at enterprise scale and of shipping AI or agentic solutions into production.
• Experience running large engineering organisations (~50–100 including partners) as a flexible pool.
• Vendor and SI management at scale, including data and delivery contracts.
• Prior senior leadership in biopharma or another regulated data environment strongly preferred; degree in a technical discipline required, advanced degree preferred.

We are proud to be a Disability Confident Employer and we encourage you to put your best self forward with the knowledge and trust that we will make any reasonable adjustments necessary to support your application and future career. Our mission is unleashing the power of our people, especially those with unique superpowers. Your journey with Pfizer starts here! We believe that a diverse and inclusive workforce is crucial to building a successful business.